In this article, the robust adaptive output tracking control problem is addressed for a class of nonlinear systems with nonlinear dynamics and unknown system parameters. The nonlinear dynamics including internal parameter uncertainties and external disturbances are formulated as time-varying state/input-dependent perturbations. Radial-basis function neural networks (RBFNNs) are developed to approximate the perturbations. A robust adaptive RBFNN-based output feedback control strategy against the perturbations is developed by using backstepping technique with immeasurable states and without knowing any system parameter. Based on Lyapunov stability theorem, the asymptotic output tracking results of the closed-loop nonlinear system are obtained in the presence of perturbations, immeasurable states, and unknown system parameters. The efficacy of the proposed adaptive RBFNN-based output feedback control strategy is validated by simulation in a DC–DC buck converter system.

中文翻译：

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一类未知系统参数扰动非线性系统的鲁棒自适应径向基函数神经网络反步控制

在本文中，针对一类具有非线性动力学和未知系统参数的非线性系统，解决了鲁棒自适应输出跟踪控制问题。包括内部参数不确定性和外部扰动的非线性动力学被表述为随时间变化的状态/输入相关的扰动。径向基函数神经网络 (RBFNN) 被开发用于近似扰动。通过使用具有不可测量状态且不知道任何系统参数的反步技术，开发了一种针对扰动的鲁棒自适应 RBFNN 输出反馈控制策略。基于李雅普诺夫稳定性定理，得到了在存在摄动、状态不可测、系统参数未知的情况下闭环非线性系统的渐近输出跟踪结果。